Teaching a Procedural Tutorial for the Dissection of the Musculoskeletal System Through the Use of a Digital Multimedia Platform
Notice bibliographique
Résumé
Introduction The use of technology is becoming synonymous with our everyday lives. This is true for both the teacher and the learner. With the advances in technology, learners are now able to choose between a variety of modalities to facilitate their learning. This is in contrast to the teachers developing new ways to appeal to all learning styles. It has been previously seen that students using both computer resources and cadavers scored better exams than students who used cadavers solely (Biasutto et al., 2006). One area that still needs to be developed further is that of human gross anatomy. In 2012, Azer S.A.'s publication showed that an online resource such as YouTube was an inadequate source of information for students and recommended that medical schools develop anatomy videos and publish them as an open source (Azer, 2012). Objectives This project was designed to create an educational video tutorial that provides a step by step visual tutorial on how to conduct a dissection. This is important because students coming to the anatomy lab are expected to read instructions that tell them to make cuts along certain parts of the body. However, these instructions fail to highlight the imagery that the students see while dissecting the body. Although there are prosections and textbooks which show the anatomy on a cadaver, they fail to show the students the steps that were taken to get to that point. The tutorial is a dissection of the anterior leg and dorsum of the foot. Methods A checklist was created to determine which structures need to be dissected and which order needs to be followed to achieve this task. Then the cadaver and filming equipment was set up. The dissection and narration were conducted and filmed. After filming, the videos were edited and animations were added to highlight the structures that were identified previously in the checklist. At the end of the video, a small quiz was added to help students test their knowledge. Results To gather the efficacy of the videos and receive feedback we gave the students a survey to conduct prior to and after their lab. The results of this survey showed that 95% of students felt that after having watched the video they were more prepared for the dissection. When asked after completing the lab, 98.7% of students felt that the videos were helpful in enhancing their learning. When asked about whether they will be using the video as a study tool for their exam 90.9% of students said yes. Some of the comments that were received include, “the videos are great in orienting students and serve as a great study tool” and “really helpful to see the dissection beforehand and the quiz questions are very helpful as study tools.” Conclusion From the positive responses that were received from the survey it can be concluded that the use of video dissections is a viable teaching tool to complement the learning that is conducted in the classroom and dissection labs. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».